Zobrazeno 1 - 10
of 74
pro vyhledávání: '"Xingyu FU"'
Publikováno v:
Meikuang Anquan, Vol 55, Iss 12, Pp 22-30 (2024)
In order to reduce the cost of coal mine filling and mining, and achieve the synergistic utilization of multi-source coal based solid waste, special filling cementitious material is prepared using solid waste such as fly ash, gasification slag, and d
Externí odkaz:
https://doaj.org/article/29093ab6c5364c4db6ec19ff2e9819bc
Publikováno v:
IEEE Journal of the Electron Devices Society, Vol 11, Pp 485-489 (2023)
In this letter, a high performance quasi-vertical GaN-on-Si Schottky barrier diode (SBD) was fabricated by combing in-situ p-GaN layer with hydrogen plasma treatment and controlled diffusion as edge terminations (ETs), where the main junction region
Externí odkaz:
https://doaj.org/article/c35f5bc0ddb4448684635e1d08111387
Autor:
Zhenghao Chen, Xuelin Yang, Xuan Liu, Jianfei Shen, Zidong Cai, Hongcai Yang, Xingyu Fu, Maojun Wang, Ning Tang, Fujun Xu, Xinqiang Wang, Weikun Ge, Bo Shen
Publikováno v:
Advanced Electronic Materials, Vol 9, Iss 7, Pp n/a-n/a (2023)
Abstract Vertical GaN‐on‐Si devices are promising for the next‐generation high‐voltage power electronics with low cost and high efficiency. However, their applications are impeded by the limited thickness of crack‐free GaN layers and high t
Externí odkaz:
https://doaj.org/article/f422679bba0846d58ff2b102144fc395
Publikováno v:
Energies, Vol 17, Iss 5, p 1027 (2024)
Cascade high-temperature heat pumps (CHTHPs) are often applied to recover low-temperature industrial waste heat owing to their large temperature lift. Through a comprehensive consideration of thermodynamic and economic performance, conventional and a
Externí odkaz:
https://doaj.org/article/dd8473598c964304bd8473c3d0a336a5
Publikováno v:
IEEE Access, Vol 8, Pp 63310-63319 (2020)
Knowledge Base Question Answering (KBQA) is a promising approach for users to access substantial knowledge and has become a research focus in recent years. Our paper focuses on relation detection, a subtask of KBQA and proposes an adversarial trainin
Externí odkaz:
https://doaj.org/article/547fbf161b2b43839681d49af585b27c
Autor:
Xinlong Li, Xingyu Fu, Guangluan Xu, Yang Yang, Jiuniu Wang, Li Jin, Qing Liu, Tianyuan Xiang
Publikováno v:
IEEE Access, Vol 8, Pp 46868-46876 (2020)
Aspect-based sentiment analysis, which aims to predict the sentiment polarities for the given aspects or targets, is a broad-spectrum and challenging research area. Recently, pre-trained models, such as BERT, have been used in aspect-based sentiment
Externí odkaz:
https://doaj.org/article/2fffd76169c546eabc8122fa27910f23
Autor:
Xinlong Li, Xingyu Fu, Guangluan Xu, Yang Yang, Jiuniu Wang, Li Jin, Qing Liu, Tianyuan Xiang
Publikováno v:
IEEE Access, Vol 8, Pp 128042-128042 (2020)
In the above article [1], the authors’ units need to be changed because of the requirements of the school.
Externí odkaz:
https://doaj.org/article/48d797b43be24dd984d6afb79166390f
Publikováno v:
Lubricants, Vol 6, Iss 4, p 98 (2018)
In recent years, surface texturing in micro-scale has been attempted on the surface of cutting tools for multiple purposes, e.g., cutting force reduction, prolonging life-span, anti-adhesion, etc. With respect to machinability and performance, micro-
Externí odkaz:
https://doaj.org/article/f2299a3817be4ae28e3e5092d852943c
Publikováno v:
Information, Vol 9, Iss 9, p 217 (2018)
Current popular abstractive summarization is based on an attentional encoder-decoder framework. Based on the architecture, the decoder generates a summary according to the full text that often results in the decoder being interfered by some irrelevan
Externí odkaz:
https://doaj.org/article/516504f82992464f8246d24d183ea90d
Publikováno v:
Algorithms, Vol 10, Iss 2, p 42 (2017)
Most of the previous works on relation extraction between named entities are often limited to extracting the pre-defined types; which are inefficient for massive unlabeled text data. Recently; with the appearance of various distributional word repres
Externí odkaz:
https://doaj.org/article/615d56d465c44e3ca4becd477d747224